This study presents a novel two-source approach for validating the performance of high density surface electromyogram (EMG) decomposition. The approach was developed taking advantage of surface EMG characteristics of amyotrophic lateral sclerosis (ALS). High density surface EMG data from ALS patients can be divided to the sparse dataset and the interference dataset, with the former decomposed by expert visual inspection while the latter independently decomposed by the surface EMG decomposition algorithm. The agreement of the decomposition yields from the two datasets can be quantified for evaluating the surface EMG decomposition performance. The novel validation approach was performed for a recently developed method called automatic progressive FastICA peel-off (APFP) for high density surface EMG decomposition. The APFP framework was used to automatically decompose high density surface EMG signals recorded from the first dorsal interosseous muscle of ALS subjects. The common motor units independently decomposed from the interference dataset and the sparse dataset demonstrated an average matching rate of 99.18% ± 1.11%. The characteristics of ALS surface EMG also facilitate a step by step illustration of the APFP framework for high density surface EMG decomposition. The novel approach presented in this study can supplement conventional two-source validation for accuracy assessment of decomposed motor units from experimental signals, which is essential for development of surface EMG decomposition methods.
展开▼
